Indoor Localization of UAV Using Monocular Vision

NIE Wei; WEN Huaizhi; XIE Liangbo; YANG Xiaolong; ZHOU Mu

doi:10.11999/JEIT211328

Volume 44 Issue 3

Mar. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(3): 906-914

NIE Wei, WEN Huaizhi, XIE Liangbo, YANG Xiaolong, ZHOU Mu. Indoor Localization of UAV Using Monocular Vision[J]. Journal of Electronics & Information Technology, 2022, 44(3): 906-914. doi: 10.11999/JEIT211328

Citation:

NIE Wei, WEN Huaizhi, XIE Liangbo, YANG Xiaolong, ZHOU Mu. Indoor Localization of UAV Using Monocular Vision[J]. Journal of Electronics & Information Technology, 2022, 44(3): 906-914. doi: 10.11999/JEIT211328

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Indoor Localization of UAV Using Monocular Vision

doi: 10.11999/JEIT211328

School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Funds: Chongqing Natural Science Foundation Project (cstc2019jcyj-msxmX0742), Chongqing Education Commission Science and Technology Research Project (KJQN202000630)

Received Date: 2021-11-21
Accepted Date: 2022-02-24
Rev Recd Date: 2022-02-23

Available Online: 2022-02-28

Publish Date: 2022-03-28

Abstract

Abstract

At present, Unmanned Aerial Vehicle (UAV) positioning technology relies mainly on the represented Global Positioning System (GPS). However, it is difficult to locate where GPS signals are missing in the room. On the other hand, the traditional indoor positioning technology uses mainly Bluetooth, WiFi, base station positioning and other methods to merge into a set of positioning system. However, this kind of methods are often affected by the environment, and they needs often to deploy multiple devices. In addition, they can only get far and near information, and can not know the device's posture in space. In this paper, an UAV indoor positioning system is proposed based on monocular vision. Firstly, the image taken by the camera is used, and combined with the feature point method and the direct method, to track the feature points first, then the direct method is used to match the features according to the key points, and then the camera position and posture are estimated. Then, the depth filter is used to estimate the 3D depth of feature points, and a sparse map in the current environment is established. Finally, the real environment is simulated using the three-dimensional visualization tool RVIZ of Robot Operating System (ROS). The simulation results show that the proposed method can achieve good performance in indoor environment, and the positioning accuracy reaches 0.04 m.
- Unmanned Aerial Vehicle (UAV),
- Indoor localization,
- Feature extraction,
- Visual Simultaneous Localization And Mapping (SLAM)

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References(19)

References

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